Predictive Modelling of Himalayan Soil Movement: Addressing Imbalance with Synthetic Variational Autoencoder Data in Kamand Valley

Due to intensifying climate change impacts, landslides have become increasingly threatening in the Himalayan region, particularly in India’s Kamand Valley. This study addresses the pressing need for accurate landslide prediction models by leveraging advanced Landslide Monitoring Systems (LMSs) and machine learning techniques. A significant challenge in developing these models is the class imbalance in soil movement data. Synthetic data generated by a Variational Autoencoder (VAE) is introduced to overcome this issue. Using VAE data, the study systematically compares various machine learning (ML) models, including Long Short-Term Memory (LSTM), Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), Convolutional LSTM (Conv-LSTM), Encoder-Decoder LSTM, alongside the novel Multi-LSTM with a Random Forest (RF) model. The ML models were trained with and without synthetic data. The test data used in the study remained intact without the incorporation of synthetic data. The results showcase the substantial impact of synthetic data on enhancing model performance. Notably, the Multi-LSTM-RF model, which integrates different LSTM architectures with an RF classifier, achieves remarkable accuracy, precision, recall, and F1 score value improvements. Furthermore, incorporating antecedent rainfall data from the preceding three days enriches the understanding of landslide dynamics. This research significantly advances landslide prediction techniques in vulnerable regions. This research significantly advances landslide prediction techniques in vulnerable regions, with the Multi-LSTM-RF model achieving an accuracy of 98.25% and an F1 score of 0.736 in testing when incorporating synthetic data, highlighting its potential for disaster preparedness and response in landslide-prone areas.